SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6626–6650 of 7282 papers

TitleStatusHype
Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for FreeCode0
TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion ModelsCode0
Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising ModelCode0
Neural Structural Correspondence Learning for Domain AdaptationCode0
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based ApproachCode0
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency ModelCode0
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applicationsCode0
Diffusion models under low-noise regimeCode0
Co-Separating Sounds of Visual ObjectsCode0
Diffusion Models with Deterministic Normalizing Flow PriorsCode0
Contrastive Learning Augmented Social RecommendationsCode0
An empirical study on the effects of different types of noise in image classification tasksCode0
Graph Adversarial Diffusion ConvolutionCode0
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter PerformanceCode0
Consistent Autoformalization for Constructing Mathematical LibrariesCode0
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translationCode0
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence ForecastingCode0
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion ModelsCode0
Adaptive Multi-step Refinement Network for Robust Point Cloud RegistrationCode0
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source SeparationCode0
Learning the optimal Tikhonov regularizer for inverse problemsCode0
Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image EnhancementCode0
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion ModelsCode0
Learning to Assimilate in Chaotic Dynamical SystemsCode0
Graph Denoising with Framelet RegularizerCode0
Show:102550
← PrevPage 266 of 292Next →
All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified